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Improved genetic prediction of complex traits from individual-level data or summary statistics

Author

Listed:
  • Qianqian Zhang

    (Aarhus University)

  • Florian Privé

    (Aarhus University)

  • Bjarni Vilhjálmsson

    (Aarhus University
    Aarhus University)

  • Doug Speed

    (Aarhus University
    Aarhus University
    Aarhus University)

Abstract

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.

Suggested Citation

  • Qianqian Zhang & Florian Privé & Bjarni Vilhjálmsson & Doug Speed, 2021. "Improved genetic prediction of complex traits from individual-level data or summary statistics," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24485-y
    DOI: 10.1038/s41467-021-24485-y
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    Cited by:

    1. Tabea Schoeler & Doug Speed & Eleonora Porcu & Nicola Pirastu & Jean-Baptiste Pingault & Zoltán Kutalik, 2023. "Participation bias in the UK Biobank distorts genetic associations and downstream analyses," Nature Human Behaviour, Nature, vol. 7(7), pages 1216-1227, July.
    2. Clara Albiñana & Zhihong Zhu & Andrew J. Schork & Andrés Ingason & Hugues Aschard & Isabell Brikell & Cynthia M. Bulik & Liselotte V. Petersen & Esben Agerbo & Jakob Grove & Merete Nordentoft & David , 2023. "Multi-PGS enhances polygenic prediction by combining 937 polygenic scores," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. David R. Blair & Thomas J. Hoffmann & Joseph T. Shieh, 2022. "Common genetic variation associated with Mendelian disease severity revealed through cryptic phenotype analysis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Jiacheng Miao & Hanmin Guo & Gefei Song & Zijie Zhao & Lin Hou & Qiongshi Lu, 2023. "Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Marion Patxot & Daniel Trejo Banos & Athanasios Kousathanas & Etienne J. Orliac & Sven E. Ojavee & Gerhard Moser & Alexander Holloway & Julia Sidorenko & Zoltan Kutalik & Reedik Mägi & Peter M. Vissch, 2021. "Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    6. Qile Dai & Geyu Zhou & Hongyu Zhao & Urmo Võsa & Lude Franke & Alexis Battle & Alexander Teumer & Terho Lehtimäki & Olli T. Raitakari & Tõnu Esko & Michael P. Epstein & Jingjing Yang, 2023. "OTTERS: a powerful TWAS framework leveraging summary-level reference data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Md. Moksedul Momin & Jisu Shin & Soohyun Lee & Buu Truong & Beben Benyamin & S. Hong Lee, 2023. "A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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